Personalized saliency means that different observers have different regions of interest in the same image. In recent years, personalized saliency has received a lot of attention in the computer vision community. Xu et al. [1]} proposed a CNN-based and personal information framework to predict a personalized saliency map. However, personalized information and images always change in the real world. Dodge et al. [2]} proposed a framework that combines the global scene information from all categories and the extracted local information to predict the saliency map. However, many categories are unknowable. Mahdi et al. [3]} utilized three CNN-based models to obtain the bottom-up and top-down deep features to predict a personalized saliency map.  However, due to the storage capacity and the computing resources, it may be difficult to utilize this framework for extracting complex features when the number of users increases. Berkovsky et al. [4]} proposed a framework for predicting personalized saliency based on eye tracking data and the framework needs to capture physiological responses, such as brain signals. However, there exist massive users in semantic communication systems and it is difficult to capture signals between multiple users, so the framework is difficult to adapt to multi-user situations. Moroto et al. [5]} proposed the framework to extract personalized saliency maps (PSMs) through Gaussian process regression. However, considering the limitation of storage capacity, the receivers, i.e., UAVs, are difficult to store the PSMs for all users. Therefore, we convert PSMs into triples to further reduce memory overhead in this paper. The above work may be unsuitable for semantic communication systems, especially for UAV scenarios. Therefore, we consider the situation of a multi-user semantic communication system and propose personalized saliency-based semantic communication.
